Method for detecting a degradation of a wheel tire

ABSTRACT

A method of detecting degradation of a real tire of a wheel includes the steps of: acquiring at least one first three-dimensional object representative of the shape of the real tire by using an electronic appliance including at least one three-dimensional sensor, the first three-dimensional object being made up of a set of capture points; determining the position of the center point of the real tire from the set of capture points; registering the first three-dimensional object to obtain a second three-dimensional object; transforming the second three-dimensional object in order to obtain one or more two-dimensional objects; and analyzing the two-dimensional object(s) in order to detect degradation of the real tire.

The invention relates to the field of methods of detecting degradationof a wheel tire.

BACKGROUND OF THE INVENTION

The tires of airplane wheels are subjected to very high stresses, inparticular during stages of takeoff and landing. On takeoff, the tiresare subjected both to the weight of the airplane and to the speed oftakeoff, and they heat up briefly, but considerably. On landing, at themoment when the wheels of the airplane touch the ground, the tires gofrom a speed of zero to a very high speed in a very short length oftime.

These phenomena tend to subject tires to wear. Unfortunately, thedeterioration of a tire can lead to excessive slip, to excessivefriction, or even to said tire bursting. It is therefore fundamental,for safety reasons, to be able to estimate the wear of tires accuratelyand reliably in order to ensure that such wear remains at a level thatis acceptable.

When the level of wear of a tire reaches a certain threshold, the tireis sent back to the factory for retreading. In general, it is tiremanufacturers who act on behalf of airlines to supply and to maintaintires. The number of tires used by airlines is very large, so it isimportant for tire manufacturers that tires are removed at the righttime, neither too soon, in order to avoid pointlessly taking usabletires out of service, nor too late, so that tire wear does not becomeexcessive.

Thus, an accurate and reliable estimate of the level of tire wear isnecessary for the safety of an airplane, and it is also advantageousfrom an economic point of view.

At present, two main methods are used by ground operatives in order toevaluate the wear of a tire.

A first method consists in visually estimating the level of wear and thedegradation of the tire.

A second method consists in manually measuring the depth of the treadsand the wear indicators of the tire.

Both of those methods give rise to a large number of errors. Theestimates and measurements that are made are subjective, and they dependto a great extent on the ground operative concerned. Proposals havetherefore been made to have the measurements taken by several groundoperatives, however such a solution is expensive and time-consuming.

Furthermore, those methods serve only to detect when the level of wearmeans that the tire needs to be replaced, and they do not serve toanticipate replacement of said tire.

Object of the Invention

One object of the invention is to detect degradation of a tire (such asexcessive wear) in reliable manner, and another is to improvemaintaining a large number of tires.

SUMMARY OF THE INVENTION

In order to achieve this object, there is provided a detection methodfor detecting degradation of a real tire of a wheel, the methodcomprising the steps of:

-   -   acquiring at least one first three-dimensional object        representative of the shape of the real tire by using an        electronic appliance including at least one three-dimensional        sensor, the first three-dimensional object being made up of a        set of capture points;    -   determining the position of a center point for the real tire        from the set of capture points;    -   registering the first three-dimensional object relative to a        theoretical tire of known dimensions and orientation in order to        obtain a second three-dimensional object forming a registered        tire;    -   transforming the second three-dimensional object in order to        obtain one or more two-dimensional objects; and    -   analyzing the two-dimensional object(s) in order to detect        degradation of the real tire.

The detection method of the invention thus makes it possible to detectdegradation of the real tire in a manner that is automatic, and thusobjective and reliable.

The detection method also makes it possible to acquire and to conservedata about the state of a large number of real tires. Data is acquiredboth simply and quickly, since in order to perform detection, itsuffices to bring the electronic appliance close to the real tire.

The detection method thus makes it possible to perform predictivemaintenance operations on a very large number of real tires, and inparticular it makes it possible to anticipate degradation andreplacement of real tires.

There is also provided a detection method as described above, whereindetermining the position of the center point of the real tire comprisesthe step of defining, for each capture point, a normal vector that isnormal to a surface of the first three-dimensional object and thatpasses through said capture point, and the step of estimating a positionfor the center point of the real tire from the normal vectors.

There is also provided a detection method as described above, whereinthe position of the center point is estimated by an iterative processduring which, on each iteration, capture points that do not belong tothe real tire are eliminated, capture points that belong to the realtire are conserved, and the estimate of the position of the center pointis refined by using the normal vectors of the capture points belongingto the real tire.

There is also provided a detection method as described above, whereinthe position of the center point of the real tire is determined byperforming a data partitioning method to distinguish between firstcapture points belonging to the real tire and second capture pointsbelonging to the ground.

There is also provided a detection method as described above, whereinregistering makes use of a registration algorithm based on Euclideantransformations.

There is also provided a detection method as described above, whereinregistering also uses an ICP algorithm.

There is also provided a detection method as described above, whereintransforming comprises the step of slicing the second three-dimensionalobject on planes perpendicular to the axis of rotation of the registeredtire in order to obtain a plurality of three-dimensional slices of smallthickness, and the step of approximating each three-dimensional slice bya two-dimensional slice of zero thickness, the two-dimensional object(s)comprising the two-dimensional slices.

There is also provided a detection method as described above, includingthe steps, for each two-dimensional slice, of calculating a radiusr_(i)(α) for the two-dimensional slice, which radius is a function of anangle α about the axis of rotation of the registered tire.

There is also provided a detection method as described above, whereinfor each two-dimensional slice, a mean radius is calculated for thetwo-dimensional slice T2 d _(i), and wherein variation of the meanradius as a function of i is investigated (where i is the index of theslice T2 d _(i)).

There is also provided a detection method as described above, whereinvariation of the mean radius is used to detect abnormal degradation ofthe tread of the real tire, as constituted by asymmetrical wear or byflattening or by bulging or by cupping or by the presence of a foreignbody.

There is also provided a detection method as described above, whereinthe positions of grooves in the tread are detected and depth isevaluated for each groove, the depths of the grooves being used todetect degradation that is excessive or abnormal, and the positions ofthe grooves are used to locate degradation that is excessive orabnormal.

There is also provided a detection method as described above, whereinthe electronic appliance further includes a photographic sensor arrangedto acquire color associated with each capture point, whereintransforming comprises two-dimensional projection applied to the secondthree-dimensional object, and wherein the two-dimensional object(s)comprise a two-dimensional image of the tread of the registered tire asobtained by said two-dimensional projection.

There is also provided a detection method as described above, whereinanalyzing the two-dimensional image comprises the step of analyzingvariation of a color gradient in the two-dimensional image.

There is also provided a detection method as described above, whereinthe analyzed variation of the color gradient is used to detect abnormaldegradation as constituted by excessive wear leading to a metal mesh ofthe real tire becoming apparent or as constituted by heat being givenoff or by contamination.

There is also provided a detection method as described above, whereinanalyzing the two-dimensional image comprises the step of applying aHough transform to the two-dimensional image.

There is also provided a detection method as described above, whereinthe Hough transform serves to detect abnormal degradation as constitutedby flattening or by bulging or by cupping or by the presence of aforeign body on the tread.

There is also provided a detection method as described above, whereinanalyzing the two-dimensional image comprises the step of performingtraining based on convolutional neural networks applied to images of thereal tire in good condition and also to images of the various looked-fordegradations.

The invention can be better understood in the light of the followingdescription of a particular, nonlimiting embodiment of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is made to the accompanying drawings, in which:

FIG. 1 shows a first three-dimensional object that is representative ofthe shape of a real tire;

FIG. 2 shows, in a two-dimensional reference frame, a method that isused for estimating the position of a center point for the real tire;

FIG. 3 shows capture points and a first estimate of the position of thecenter point;

FIG. 4 is a histogram showing the distribution of distances between thecapture points and the first estimate of the position of the centerpoint;

FIG. 5 shows the capture points and a final estimate of the position ofthe center point;

FIG. 6 is a histogram showing the distribution of distances between thecapture points and the final estimate of the position of the centerpoint;

FIG. 7 shows the implementation of a data partitioning method;

FIG. 8 shows, the real tire and a theoretical tire prior toregistration;

FIG. 9 shows, after registration, a second three-dimensional object asobtained by registering the first three-dimensional object and forming aregistered tire;

FIG. 10 shows three-dimensional slices and two-dimensional slices of aregistered tire;

FIG. 11 shows the tread of a real tire, showing normal wear;

FIG. 12 is a graph plotting a curve showing how the mean radius of theregistered tire corresponding to the real tire of FIG. 11 variesrelative to the axis of rotation;

FIG. 13 shows the tread of a real tire that presents asymmetrical wear;

FIG. 14 is a graph plotting a curve showing how the mean radius of theregistered tire corresponding to the real tire of FIG. 13 variesrelative to the axis of rotation;

FIG. 15 shows the tread of a real tire in which cupping has formed;

FIG. 16 is a graph plotting a curve showing how the mean radius of theregistered tire corresponding to the real tire of FIG. 15 variesrelative to the axis of rotation;

FIG. 17 shows the tread of a real tire that presents flattening;

FIG. 18 is a graph plotting a curve showing how the mean radius of theregistered tire corresponding to the real tire of FIG. 17 variesrelative to the axis of rotation;

FIG. 19 shows the tread of a real tire having a nail plunged therein;

FIG. 20 shows the tread of a real tire that once had a nail plungedtherein, after the nail has been removed, leaving a hole;

FIG. 21 is a graph plotting a curve showing how the mean radius of theregistered tire corresponding to the real tire of FIG. 20 variesrelative to the axis of rotation;

FIG. 22 shows the tread of a real tire, in which there can be seen twogrooves and one hole;

FIG. 23 shows the application of a Hough transform to a two-dimensionalimage corresponding to FIG. 22;

FIG. 24 shows the tread of a real tire, in which the metal mesh of thereal tire has become apparent;

FIG. 25 shows the color gradients of FIG. 24;

FIG. 26 shows the application of a Hough transform to a two-dimensionalimage of a real tire having flattening formed on its tread;

FIG. 27 shows the tread of a real tire that is contaminated;

FIG. 28 shows the variation of the color gradient in a two-dimensionalimage corresponding to a real tire that is not contaminated;

FIG. 29 shows the variation of the color gradient in a two-dimensionalimage corresponding to a real tire that is contaminated;

FIG. 30 shows the use of a contour-detection filter in HSV space,implemented on a two-dimensional image obtained from the tread of FIG.19;

FIG. 31 shows training steps based on neural networks and performed ontwo-dimensional images; and

FIG. 32 is a contour detection matrix.

DETAILED DESCRIPTION OF THE INVENTION

The detection method of the invention is performed below for detectingdegradation of a real tire of an airplane wheel. The term “real tire” isused to mean a genuine tire mounted on the wheel, as contrasted to a“virtual” tire such as the theoretical tire and the registered tire thatare referred to below.

With reference to FIG. 1, the detection method starts with a step ofacquiring at least a first three-dimensional object 1 that isrepresentative of the shape of the real tire, by using an electronicappliance that includes both a three-dimensional sensor and aphotographic sensor.

By way of example, the electronic appliance may be a smartphone that isbrought up to the real tire by a ground operative.

By way of example, the three-dimensional sensor is a stereo cameraadapted to perform a three-dimensional scan function. By way of example,the first three-dimensional object 1 is a three-dimensional image.

The first three-dimensional object 1 is formed by a set of capturepoints (also referred to as a “cloud” of capture points).

The smartphone acquires and stores the first three-dimensional object 1.

It should be observed that it is possible to use any type ofthree-dimensional sensor that is capable of obtaining athree-dimensional object that is made up of points and that representsthe shape of the real tire. It should also be observed that theelectronic appliance could naturally be other than a smartphone. By wayof example, a 3D camera connected to a computer could be used.

The photographic sensor serves to acquire color that is associated witheach capture points.

Each capture point is made up of three-dimensional Cartesian coordinates(x, y, z). The capture points represent the scanned portion of the realtire.

The recording presents capture points at a density that is high enoughfor it to be possible to perform accurate calculations on the basis ofthe capture points. In this example, the density is 60 capture pointsper square centimeter on average.

Below, a fixed reference frame is used, specifically a cylindricalcoordinate system, in which the axis of rotation of the real tire iscaused to correspond to a straight line having the followingcoordinates:

(r, Θ, z)=(0, 0, z)

The position of the center point of the real tire is determined from theset of capture points. The position of the center point of the real tireserves to enable the real tire to be extracted from the firstthree-dimensional object 1, and thus serves to eliminate thesurroundings of the real tire from the first three-dimensional object 1.These surroundings include in particular the ground on which the realtire is standing.

In a first implementation, the position of the real tire is defined froman estimate of the position of the center point of the real tire. Thecenter point of the real tire is a point that is situated “at thecenter” of the real tire. The center point is situated where the axis ofrotation of the real tire intersects the plane of symmetry of the realtire that is perpendicular to the axis of rotation.

The position of the center point is estimated as follows.

The capture points of the recording are situated either on a surface ofthe real tire, or else on a surface of the ground. Each capture pointthus forms a vertex positioned on a surface. The recording provided bythe three-dimensional sensor produces the (x, y, z) coordinates of eachvertex in a rectangular reference frame at life size. The center of thisrectangular reference frame is determined by the three-dimensionalsensor.

In order to simplify calculation, the capture points, or vertices, arenormalized in [0, 1] in order to have a reference frame that isintrinsic to the data. This conserves a common scale for all of thecapture points, and facilitates defining the normalizations performed inthe subsequent calculations.

In order to produce the normalized vertices, the following calculationis performed:

Pnorm  :=  (P − min (P))/max (P)

where:

-   -   Pnorm is the set of normalized vertices having the coordinates        (xpnorm, ypnorm, zpnorm), which are normalized in [0, 1]    -   P is the set of non-normalized vertices, having the coordinates        (xp, yp, zp);    -   min(P) is the minimum value of xp, yp, and zp; and    -   max(P) is the maximum value of xp, yp, and zp.

The three-dimensional sensor also serves, for each vertex, to determinethe vectors that are normal to the surface (of the real tire or of theground). Each normal vector has a norm equal to 1.

The normalized vertices and the normal vectors are used to evaluate theposition of the center point.

With reference to FIG. 2, the circle 2 is an approximation to a view ofthe tread of the real tire as seen in section on a plane perpendicularto the axis of rotation of the real tire. The circle 2 thus defines thesurface of the real tire at its tread. The circle 2 has a center 3.

Likewise, lines 4 define the surface of the ground.

By definition, each normal vector ni that is normal to the surface ofthe real tire and that passes through a capture point Pi situated on thecircle 2 points away from the center 3 of said circle 2. The vector −nithat is opposite to the normal vector thus points towards the center 3of the circle 2, which is an estimate of the position of the centerpoint.

In contrast, the normal vectors that are normal to the surface of theground and that pass through capture points Pi situated on the lines 4point in other directions.

The position Pi−ni/2 is thus calculated for each capture point Pi.

For the great majority of capture points Pi, which are situated on thetread (i.e. on the circle 2 of FIG. 2), these positions lie within afirst zone 5 that is close to the center point 3.

For the “interfering” capture points Pi, which belong to the ground onwhich the real tire stands, these positions lie within a second zone 6that is remote from the center point 3.

The estimated center point 3 is the geometrical mean of the pointssituated in the first zone 5 and in the second zone 6, with thisapplying on each iteration.

The position of the center point is estimated by using an iterativeprocess during which, on each iteration, capture points that do notbelong to the real tire (and that thus comprise the capture pointsbelonging to the ground) are eliminated, while capture points thatbelong to the real tire are conserved, and the estimate of the positionof the center point is refined by using the normal vectors of thecapture points belonging to the real tire.

With reference to FIG. 3, a first estimate Pc₁ for the position of thecenter point is obtained from the normal vectors:

$\begin{matrix}{{Pc}_{1} = {\left( {{\Sigma\;{Pi}} - {\Sigma\;\overset{\rightarrow}{ni}\text{/}2}} \right)\text{/}N_{points}}} \\{= {\left( {\Sigma\left( {{Pi} - {\overset{\rightarrow}{ni}\text{/}2}} \right)} \right)\text{/}{N_{points}.}}}\end{matrix}$

where N_(points) is the number of capture points under consideration.

The distance between each capture point and the first estimate Pc₁ forthe position of the center point is then calculated.

Assuming that the ground operative uses the smartphone to acquire mainlythe real tire, and assuming that the real tire is spherical in shape,the capture points are concentrated on a given radius.

This can be seen clearly in FIG. 4. The peak of histogram portion 7corresponds to the tread of the real tire, while histogram portion 8corresponds to the surroundings of the real tire, and in particular tothe capture points that are situated on the ground.

The capture points that do not belong to the peak of histogram portion 7are then eliminated and the position of the center point is calculatedonce more. This serves to limit the set of capture points to a zonecovering the radius that appears the most frequently.

A second estimate PC₂ is thus obtained for the position of the centerpoint.

This process is repeated until the following relationship is obtainedbetween the current estimate Pc_(n) for the position of the center pointand the estimate Pc_(n-1) that was obtained at the preceding iteration:

Pc_(n) − Pc_(n − 1) < ɛ

where ε is a predetermined stop threshold, e.g. equal to 0.001 meters.

The final estimate Pc_(f) for the position of the center point is thusobtained, as can be seen in FIG. 5.

On the basis of the distances between the capture points and the finalestimate Pc_(f) for the position of the center point, FIG. 6 showsclearly a histogram portion 9 that corresponds to the real tire and ahistogram portion 10 that corresponds to the ground.

The capture points belonging to the real tire are then isolated from thecapture points belonging to the surroundings of the real tire (and inparticular on the ground), with only the capture points belonging to thereal tire being conserved.

It is possible to define the position of the tread from the sphericalshape of the real tire and from the final estimate Pc_(f) for theposition of the center point. This is based on the principle that all ofthe capture points of the tread are situated at a constant distance Rfrom the center point.

The position of the real tire is thus obtained.

In a second implementation, and with reference to FIG. 7, the positionof the real tire is determined by performing a data partitioning methodto distinguish between first capture points 11 belonging to the realtire and second capture points 12 belonging to the ground.

Data partitioning (also known as data “clustering”) is an analysismethod that serves to subdivide a dataset into a plurality of sub-setsthat share common characteristics.

When detecting the position and the orientation of the real tire in theset of capture points, it is postulated that the portion of the realtire that is in contact with the ground cannot be captured. This impliessome form of “cut-off” between the first capture points 11 and thesecond capture points 12.

In this example, the data is partitioned by using a DBSCAN algorithm(for “density-based spatial clustering of applications with noise”).

As can be seen in FIG. 7, the first capture points 11 belonging to thereal tire are clearly distinguished from the second capture points 12belonging to the ground: this enables the position of the real tire tobe determined effectively.

With reference to FIGS. 8 and 9, once the position of the real tire 13has been estimated, the first three-dimensional object 13 is registeredrelative to a theoretical tire 14 in order to obtain a secondthree-dimensional object 15 that forms a “registered” tire. The secondthree-dimensional object 15 is made up of a set of registered points.

To do this, the number of capture points is initially reduced bysampling.

Thereafter, on the basis solely of a portion of the tread and of aportion of the sidewalls of the real tire, a registration algorithm isapplied for registering relative to a cloud of theoretical points thatcorrespond to the theoretical tire 14.

Registration serves to determine the axis of rotation of the real tire.The theoretical tire 14 is created specifically for registeringpurposes. In particular, the theoretical tire 14 presents dimensions andan orientation that are known. Use is made in particular of input dataincluding the axis of rotation and the center point of the theoreticaltire 14.

Registration makes use of a registration algorithm based on Euclideantransformations.

Euclidean transformations (rotation, translation) are performed towardsa known reference frame, which do not transform the object. Euclideantransformations are defined by:

T(v) = R  v + t,

where:

-   -   v is a vector of the object;    -   R is an orthogonal transformation; and    -   t is a translation vector.

Registration consist in finding the transformation that serves tominimize the difference between the cloud of capture points and thecloud of theoretical points. In this example, use is made of analgorithm of the iterative corresponding point (ICP) type.

There follows a description of the processing performed on the secondthree-dimensional object 15 forming the registered tire.

This processing consists in transforming the second three-dimensionalobject 15 forming the registered tire so as to obtain one or moretwo-dimensional objects, and then in analyzing the two-dimensionalobject(s) in order to detect degradation of the real tire.

With reference to FIG. 10, in a first implementation, the transformationconsists initially in slicing the second three-dimensional object 15 onplanes perpendicular to the axis of rotation Z of the registered tire inorder to obtain a number n of thin three-dimensional slices T3 d ₁ . . .T3 d _(i) . . . T3 d _(n). The thickness e of each three-dimensionalslice T3 d _(i) is of millimeter order.

A number n of the three-dimensional slices T3 d _(i) is obtained thatmake up the registered tire when they are stacked together along theaxis of rotation Z of the registered tire.

The sum of the thicknesses of all of the three-dimensional slices T3 d_(i) is thus equal to the width of the tread of the registered tire.

The thickness of each three-dimensional slice T3 d _(i) is very smallcompared with the radius of the registered tire.

Each three-dimensional slice T3 d _(i) is thus approximated by atwo-dimensional slice T2 d _(i), of zero thickness. The two-dimensionalobjects that result from the transformation thus comprise thetwo-dimensional slices T2 d _(i).

Each two-dimensional slice T2 d _(i) is thus generally in the shape of acircle if the smartphone acquires the entire circumference of the realtire, or in the shape of an angular portion of a circle if thesmartphone acquires only an angular portion of the circumference of thereal tire.

A radius r_(i)(α) is then calculated for each two-dimensional slice T2 d_(i). The radius r_(i)(α) is a function of an angle α about the axis ofrotation z. The radius r_(i)(α) is not necessarily constant as afunction of the angle α, since the wear of the real tire is notnecessarily uniform around the entire circumference of thetwo-dimensional slice T2 d _(i).

Variation in the radius r_(i)(α) is then investigated by varying i inorder to detect any difference in radius of the registered tire alongthe axis of rotation Z (i.e. across the width of the tread), and thus todetect any degradation of the real tire.

The differences between the radii r_(i)(α) for varying i should thenpresent variations that are regular, representative of the presence ofstructures of the real tire, and the depths of the structures can thenbe quantified.

The presence of grooves in the tread of the real tire should thus beperceptible, which grooves are presently used for visually evaluatingthe wear of the real tire.

Naturally, this analysis may be performed at constant α, or else bycausing the angle α to vary over a defined angular interval.

It is also possible to calculate the mean the radius r_(i) of eachtwo-dimensional slice T2 d _(i). Variation in the mean radius r_(i) isthen investigated as a function of i, i.e. along the axis of rotation z.

Variation in the radius r_(i)(α) with varying angle α is alsoinvestigated in order to detect angular variations in the radius of theregistered tire, and thus in order to detect degradation of the realtire.

For a given two-dimensional slice T2 d _(i), the radius r_(i)(α) shouldnormally present very little variation as a function of the angle α,which implies wear that is regular.

If this is not so, then the coordinates (r, Θ, z) of the capture pointsserve to provide information about the presence, the shape, and thedepth of an anomaly.

One or more parameters are then analyzed from among the variation of theradius r_(i)(α) as a function of i, the variation of the radius r_(i)(α)as a function of α, and the variation of the mean radius r_(i) as afunction of i. By studying these radius variations, it is possible todetermine an unexpected change, such as a localized or continuousreduction of this radius.

FIG. 11 shows the tread 20 of a real tire 21 that presents normal wear.It can be seen that the four grooves 22 are worn symmetrically andregularly around the axis of rotation of the real tire 21.

The curve 23 in FIG. 12 is a plot as a function of the value of ishowing variation in the mean radius r_(i) of the registered tire thatcorresponds to the real tire 21 of FIG. 11.

From the curve 23, there can be seen four curved portions 24, eachcorresponding to a small mean radius r_(i) and to a respective one ofthe four grooves 22 present in the tread 20 of the real tire 21 (on thegraph, the grooves 22 are represented by bold lines showing thepositions of said grooves 22 across the width of the tread 20).

It is thus possible to detect the positions of the grooves 22 on thetread 20 and to evaluate the depth of each groove 22.

Evaluating the depth of each groove 22 can make it possible to detectthe presence of degradation of the tread 20.

Detecting the positions of the grooves 22 serves to position thedegradation on the tread 20.

In this example, the tread 20 does not present any abnormal degradation.The wear of the real tire 21 is normal and symmetrical, and each of thegrooves 22 has a depth that is more or less constant.

FIG. 13 shows the tread 26 of a real tire 27, which tread presents wearthat is abnormal, asymmetrical, and poorly distributed across the tread26.

The curve 28 of the graph in FIG. 14 shows that the grooves 29 and 30present depths that are very small compared with the other grooves. Themean radius of the registered tire varies very little in these grooves,which is representative of wear that is excessive and not symmetrical,since the wear relates to two grooves both situated on the same side ofthe tread 26.

FIG. 15 shows that cupping 32 has formed in the tread 33 of a real tire34, between the groove 35 and the groove 36.

The curve 38 in FIG. 16 shows that the mean radius decreases in thecurved portion 37 that lies between the two grooves 35 and 36, which isrepresentative of the presence of cupping.

Clear or repeated variations in the mean radius may specifically beindicative of a surface presenting anomalies of the following types:cupping, bulging, abnormal excrescences, tearing, zig-zags, cracking,etc.

FIG. 17 shows the presence of flattening (i.e. a flat spot) 40 presenton the tread 41 of a real tire 42. This flat spot 40 is a result ofbraking without the real tire 42 rotating.

The flat spot 40 can be seen in the curve 45 of the graph of FIG. 18.Over a zone that is relatively flat, curve portion 46 presents asuccession of small-amplitude peaks and troughs that are representativeof the presence of the flat spot.

A curve portion of reduced mean radius cannot be seen: the grooves 47are not detected because of the flat spot 40.

With reference to FIG. 19, it is also possible to detect the presence ofa foreign body in the outer layer of the tread 50 of a real tire 51. Byway of example, the foreign body is a nail 52 plunged into the tread 50.The presence of the nail 52 is detected by identifying a localizedchange in the mean radius of the registered tire within a giveninterval:

[(r, Θ, z), (r + δ1, Θ + δ2, z + δ3)]

where [|δ1|, |δ2|, |δ3|] represent the dimensions of the emergentportion of the nail 52.

It should be observed that it is also possible to make use of color datain order to determine a localized difference exceeding a certainthreshold, providing the foreign body presents a color or a brightnessthat is significantly different from the rubber of the real tire.

It is also possible to envisage using a specific sensor, e.g. anultrasound sensor, in order to detect a change of material that isrepresentative of the presence of a foreign body or indeed of the metalmesh of the tire becoming apparent.

It is also possible to detect an anomaly by analyzing a temperaturegradient by using a suitable thermal camera, possibly incorporated inthe smartphone.

FIG. 20 shows the tread 54 of a real tire 55 that has had a nail plungetherein, thereby forming a hole 56, with the nail subsequently becomingextracted from the tread 54.

It can be seen from the curve 57 of the graph in FIG. 21 that the meanradius of the registered tire decreases considerably in the curveportion 58 between the two grooves 59, with this being representative ofthe presence of the hole formed by the nail.

As described above, in the first implementation, the transformation ofthe second three-dimensional object of the registered tire producestwo-dimensional objects that are two-dimensional slices T2 d _(i) of theregistered tire. In a second implementation, the transformation is atwo-dimensional projection of the three-dimensional object 15. Thetwo-dimensional objects include a two-dimensional image of the tread ofthe registered tire as obtained by performing said two-dimensionalprojection.

The two-dimensional objects also include respective two-dimensionalimages of the right sidewall and of the left sidewall of the registeredtire. In order to obtain the images of the sidewalls, the shape of thesidewalls means that no two-dimensional projection is needed.

In order to perform a two-dimensional projection, use is made ofcylindrical coordinates together with the following formulae:

x = R ⋅ (λ − λ0) y = R ⋅ (tan (ϕ))

λ corresponds to a current point of the projection, and λ0 correspondsto a point set as the origin of the reference frame.

As mentioned above, colors are also available in association with thecapture points in the set of acquired capture points stored by thesmartphone.

The two-dimensional images is analyzed by analyzing variation in a colorgradient of the two-dimensional image and/or by applying a Houghtransform to the two-dimensional image.

Analyzing color gradient variation serves to detect abnormal degradationthat results in abnormal variation of the colors of the tread, while theHough transform serves to detect contours, and thus to detect abnormaldegradation that results in abnormal portions in relief on the tread.

FIG. 22 shows the tread 60 of a real tire 61 on which there can be seentwo grooves 62 and a hole 63. Two-dimensional projection is applied tothe second three-dimensional object in order to obtain a two-dimensionalimage of the tread. The Hough transform is then applied to the image inorder to obtain the image of FIG. 23.

It is considered that each groove is defined by two lines in atwo-dimensional plane, each line corresponding to a respective edge ofsaid groove. The Hough transform thus makes it possible to detect thegrooves 62, each of which is defined by two lines 64, and also to detectthe hole 63.

FIG. 24 shows the tread 66 of a real tire 67, with the metal mesh 68 ofthe real tire 67 being visible. The metal mesh 68 is visible because ofexcessive wear of the real tire 67. By way of example, the excessivewear may be due to an excessive amount of heat being given off over aportion of the real tire 67 as a result of a braking problem, a runwaythat is wet or frozen, etc.

FIG. 25 shows variation in the color gradient in the two-dimensionalimage.

A zone 69 can be identified in which abnormal color differences are tobe observed, which in this example are representative of the metal mesh68 becoming apparent.

With reference to FIG. 26, a tread may include a flat spot. The flatspot is characterized by a circular shape on the tread.

The two-dimensional image of the tread is produced, the two-dimensionalimage is then processed in order to remove noise that might impede adetection, and then the Hough transform is applied to thetwo-dimensional image. More specifically, in this example, a circleHough transform (CHT) type algorithm is applied.

The Hough transform serves to obtain a shape 70 that is representativeof a flat spot.

With reference to FIG. 27, the tread 71 of a real tire 72 may also bedegraded by wear associated with heat being given off or withcontamination by a corrosive liquid (oil, brake fluid, etc.).

In the event of damage of this type, variation of the color gradient isused to identify zones that present granularity that is abnormal. Sincethe real tire 72 is made out of a single material, it can be assumedthat if the real tire 72 is in a normal state, then any observed colorgradient will be simple, and associated solely with external factorssuch as exposure to the sun. In contrast, the presence of discontinuityin the linearity of the color gradient, or indeed the presence of zonesthat present colors that differ from the colors of the remainder of thereal tire and that are not associated with exposure to external light,are signs of degradation of the rubber layer of the tread 71.

FIG. 28 shows how the color gradient varies when the real tire is notcontaminated. FIG. 29 shows how the color gradient varies when the realtire is contaminated.

The differences between these two figures showed clearly that colorgradient variation is a parameter that is relevant for detectingcontamination of a real tire.

With reference to FIG. 30, by using a filter for detecting contours inhue-saturation-value (HSV) space, it is possible to detect a foreignbody in the real tire, and also a groove of the real tire.

The shape 75 corresponds to the nail 52 in FIG. 19, while the shape 76corresponds to the groove situated immediately to the left of the nail52 in FIG. 19.

The two-dimensional images obtained by two-dimensional projection of thesecond three-dimensional object 15 of the registered tire can also beanalyzed by training neural networks on the basis of images of the realtire in good condition and of images of various kinds of degradationthat are to be found.

Use is made of a database having a very large amount of data constitutedby similar two-dimensional images acquired from a very large number ofreal tires.

Since the two-dimensional projections are performed in a standardizedmanner, the two-dimensional images can be compared effectively, whileeliminating the differences that are due to the ways in which the datais acquired (and thus differences that are not relevant for analysis).

During a preliminary stage, the data needs to be classified manuallyinto a plurality of distinct categories.

Convolutional neural networks (CNN or CovNets) are used to provide theprediction algorithm.

With reference to FIG. 31, for a given two-dimensional image, trainingcomprises an initial step of convoluting the two-dimensional image witha predefined matrix (step E1). In this example, the contour detectionmatrix of FIG. 32 is used.

Thereafter, a correction function is applied.

By way of example, the function used is of the rectified linear unit(ReLu) type.

f(x) = max (0, x);

or else a hyperbolic tangent function:

f(x) = tanh (x);

or else a saturating hyperbolic tangent function:

f(x) = tanh (x)

or else a sigmoid function:

f(x) = (1 + e^(−x))⁻¹.

Thereafter, a pooling (or, commoning) operation is performed to reducethe size of the two-dimensional image (step E2).

It is then possible to process each of the zones of the two-dimensionalimage in individual manner via an artificial neural network (step E3).

The last step (step E4) consists in applying the Softmax formula:

$\begin{matrix}{P_{j} = \frac{e^{O_{j}}}{\Sigma_{k}\mspace{14mu} e^{O_{k}}}} & \left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack\end{matrix}$

The input layers of the neural network are thus converted into adistribution probability. It is then possible to classify thetwo-dimensional image data and to detect abnormal degradation in thetwo-dimensional images.

It is also possible to implement neural network training directly on thesecond three-dimensional objects of the registered tire.

In a first step, once again, the data needs to be classified manuallyinto a plurality of distinct categories.

In order to prepare the prediction algorithm, use is made likewise ofconvolutional neural networks (CNN or CovNets).

It should be observed at this point that, when degradation of a realtire is detected, a warning message is generated. The warning messagemay be issued by the smartphone for the attention of the groundoperative, or else it may be transferred from the smartphone to a serverthat centralizes warning messages coming from a large number of realtires (e.g. all of the real tires used by an airline).

The warning message contains means for identifying the tire in question(e.g. a reference of the tire or its position on the airplane),optionally together with the type of the degradation and its position onthe tire.

The set of capture points together with the results of the analysesperformed are stored, and they can be used for the purposes ofverification and data analysis on a large scale. Such analysis mayreveal correlations between excessive deterioration of tires andparticular runways, particular takeoff, landing, or taxiing situations,particular weather, the way certain pilots pilot airplanes, etc.

Naturally, the invention is not limited to the implementationsdescribed, but covers any variant coming within the ambit of theinvention as defined by the claims.

1. A method of detecting a degradation of a real tire of a wheelstanding on the ground, the method comprising the steps of: acquiring atleast one first three-dimensional object representative of a shape ofthe real tire by using an electronic appliance including at least onethree-dimensional sensor, the first three-dimensional object being madeup of a set of capture points; distinguishing between capture pointsbelonging to the real tire and capture points belonging to the ground,and determining a position for a center point of the real tire from theset of capture points belonging to the real tire; registering the firstthree-dimensional object relative to a theoretical tire of knowndimensions and orientation in order to obtain a second three-dimensionalobject forming a registered tire, the registration consisting in findinga transformation that serves to minimize the difference between the setof capture points and a cloud of theoretical points corresponding to thetheoretical tire; transforming the second three-dimensional object inorder to obtain one or more two-dimensional objects; and analyzing thetwo-dimensional object(s) in order to detect degradation of the realtire.
 2. The detection method according to claim 1, comprising the stepof defining, for each capture point, a normal vector that is normal to asurface of the first three-dimensional object and that passes throughsaid capture point, and the step of estimating the position of thecenter point of the real tire from the normal vectors.
 3. The detectionmethod according to claim 2, wherein the position of the center point isestimated by an iterative process during which, on each iteration,capture points that do not belong to the real tire are eliminated,capture points that belong to the real tire are conserved, and theestimate of the position of the center point is refined by using thenormal vectors of the capture points belonging to the real tire.
 4. Thedetection method according to claim 1, comprising a step of performing adata partitioning method to distinguish between the capture pointsbelonging to the real tire and the capture points belonging to theground.
 5. The detection method according to claim 1, whereinregistering makes use of a registration algorithm based on Euclideantransformations.
 6. The detection method according to claim 5, whereinregistering also uses an ICP algorithm.
 7. The detection methodaccording to claim 1, wherein transforming comprises the step of slicingthe second three-dimensional object on planes perpendicular to the axisof rotation of the registered tire in order to obtain a plurality ofthree-dimensional slices of small thickness, and the step ofapproximating each three-dimensional slice by a two-dimensional slice ofzero thickness, the two-dimensional object(s) comprising thetwo-dimensional slices.
 8. The detection method according to claim 7,comprising the steps, for each two-dimensional slice, of calculating aradius ri(α) for the two-dimensional slice, which radius is a functionof an angle α about the axis of rotation of the registered tire.
 9. Thedetection method according to claim 8, wherein for each two-dimensionalslice, a mean radius is calculated for the two-dimensional slice, andwherein variation of the mean radius as a function of i is investigated,where i is the index of the slice.
 10. The detection method according toclaim 9, wherein variation of the mean radius is used to detect abnormaldegradation of the tread of the real tire, as constituted byasymmetrical wear or by flattening or by bulging or by cupping or by thepresence of a foreign body.
 11. The detection method according to claim10, wherein the positions of grooves in the tread are detected and depthis evaluated for each groove, the depths of the grooves being used todetect degradation that is excessive or abnormal, and the positions ofthe grooves are used to locate degradation that is excessive orabnormal.
 12. The detection method according to claim 1, wherein theelectronic appliance further includes a photographic sensor arranged toacquire color associated with each capture point, wherein transformingcomprises two-dimensional projection applied to the secondthree-dimensional object, and wherein the two-dimensional object(s)comprise a two-dimensional image of the tread of the registered tire asobtained by said two-dimensional projection.
 13. The detection methodaccording to claim 12, wherein analyzing the two-dimensional imagecomprises the step of analyzing variation of a color gradient in thetwo-dimensional image.
 14. The detection method according to claim 13,wherein the analyzed variation of the color gradient is used to detectabnormal degradation as constituted by excessive wear leading to a metalmesh of the real tire becoming apparent or as constituted by heat beinggiven off or by contamination.
 15. The detection method according toclaim 12, wherein analyzing the two-dimensional image comprises the stepof applying a Hough transform to the two-dimensional image.
 16. Thedetection method according to claim 15, wherein the Hough transformserves to detect abnormal degradation as constituted by flattening or bybulging or by cupping or by the presence of a foreign body on the tread.17. The detection method according to claim 12, wherein analyzing thetwo-dimensional image comprises the step of performing training based onconvolutional neural networks applied to images of the real tire in goodcondition and also to images of the various looked-for degradations.